Electrochemical imaging for microfluidics: a full-system approach
Bibliographic record
Abstract
Electrochemistry is developed as a new chemical imaging modality for microfluidics. The technique is based on multipoint voltammetry using an embedded 20 × 10 miniature electrode array implemented on a customized printed circuit board. Electrode durability was enhanced by chemical modification of the electrode surfaces, which enabled continuous, stable use for over 2 months. A system-level approach enables automatic calibration, data acquisition and data processing through a graphical user interface. Following data processing, redox currents and peak positions are extracted from location-specific voltammograms and converted into pixels of an "electrochemical image". The system is validated by imaging steady-state and dynamic laminar flow patterns of flow-confined solutions of the redox pairs Fe(CN)6(3-/4-) or multi-redox environments that include coflowing Ru(NH3)6(2+/3+) solutions. The images obtained are compared with flow simulations and optical images for validation. A strategy to achieve measurements with spatial resolution smaller than the individual electrodes is also demonstrated as an avenue to enhance image spatial resolution. It is expected that this new approach to chemical imaging will expand the applicability of microfluidics in certain areas of chemistry and biology without requiring expertise in electrochemistry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".